Overview

Dataset statistics

Number of variables12
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qtde_invoicesHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtde_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 25.15706781)Skewed
frequency is highly skewed (γ1 = 24.87675009)Skewed
qtde_returns is highly skewed (γ1 = 21.9754032)Skewed
customer_id has unique valuesUnique
recency_days has 33 (1.1%) zerosZeros
qtde_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-03-06 21:25:23.847857
Analysis finished2023-03-06 21:25:44.102597
Duration20.25 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2968
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.377
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:44.198340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.1445
Coefficient of variation (CV)0.11258036
Kurtosis-1.2061782
Mean15270.377
Median Absolute Deviation (MAD)1489
Skewness0.032193711
Sum45322479
Variance2955457.9
MonotonicityNot monotonic
2023-03-06T18:25:44.346984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
12670 1
 
< 0.1%
17734 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
Other values (2958) 2958
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2953
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.3894
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:44.509551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.326
Coefficient of variation (CV)3.7630378
Kurtosis397.31841
Mean2693.3894
Median Absolute Deviation (MAD)671.39
Skewness17.635745
Sum7993979.7
Variance1.0272483 × 108
MonotonicityNot monotonic
2023-03-06T18:25:44.658153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1078.96 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
1353.74 2
 
0.1%
889.93 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
734.94 2
 
0.1%
Other values (2943) 2948
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
140438.72 1
< 0.1%
124564.53 1
< 0.1%
117375.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%
65019.62 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.31031
Minimum0
Maximum373
Zeros33
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:44.823178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.760314
Coefficient of variation (CV)1.2091423
Kurtosis2.7765932
Mean64.31031
Median Absolute Deviation (MAD)26
Skewness1.7980702
Sum190873
Variance6046.6664
MonotonicityNot monotonic
2023-03-06T18:25:44.975878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2218
74.7%
ValueCountFrequency (%)
0 33
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7240566
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:45.140512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8578826
Coefficient of variation (CV)1.5474834
Kurtosis190.77715
Mean5.7240566
Median Absolute Deviation (MAD)2
Skewness10.765206
Sum16989
Variance78.462084
MonotonicityNot monotonic
2023-03-06T18:25:45.286409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_items
Real number (ℝ)

Distinct1664
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1579.7123
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:45.439639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.35
Q1296
median638
Q31398.25
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1102.25

Descriptive statistics

Standard deviation5700.53
Coefficient of variation (CV)3.6085875
Kurtosis518.12284
Mean1579.7123
Median Absolute Deviation (MAD)419
Skewness18.760258
Sum4688586
Variance32496042
MonotonicityNot monotonic
2023-03-06T18:25:45.597260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
246 8
 
0.3%
516 7
 
0.2%
394 7
 
0.2%
Other values (1654) 2885
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
79963 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
62812 1
< 0.1%
58243 1
< 0.1%
57785 1
< 0.1%
50255 1
< 0.1%

qtde_products
Real number (ℝ)

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.74562
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:45.763817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.87852
Coefficient of variation (CV)2.1986814
Kurtosis354.75508
Mean122.74562
Median Absolute Deviation (MAD)44
Skewness15.70464
Sum364309
Variance72834.414
MonotonicityNot monotonic
2023-03-06T18:25:45.925387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 45
 
1.5%
20 38
 
1.3%
35 35
 
1.2%
15 33
 
1.1%
29 33
 
1.1%
19 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 29
 
1.0%
Other values (459) 2629
88.6%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 15
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 27
0.9%
10 27
0.9%
ValueCountFrequency (%)
7837 1
< 0.1%
5670 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2965
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.996553
Minimum2.1505882
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:46.091954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.915888
Q113.118111
median17.965485
Q324.981794
95-th percentile90.052125
Maximum4453.43
Range4451.2794
Interquartile range (IQR)11.863683

Descriptive statistics

Standard deviation119.53182
Coefficient of variation (CV)3.6225547
Kurtosis812.96961
Mean32.996553
Median Absolute Deviation (MAD)5.9806694
Skewness25.157068
Sum97933.769
Variance14287.855
MonotonicityNot monotonic
2023-03-06T18:25:46.236129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2955) 2955
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%
615.75 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.305053
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:46.392744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.927198
median48.267857
Q385.333333
95-th percentile200.65
Maximum366
Range365
Interquartile range (IQR)59.406136

Descriptive statistics

Standard deviation63.503259
Coefficient of variation (CV)0.94351399
Kurtosis4.9086453
Mean67.305053
Median Absolute Deviation (MAD)26.267857
Skewness2.0662224
Sum199761.4
Variance4032.6639
MonotonicityNot monotonic
2023-03-06T18:25:46.547327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
11 17
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
28 16
 
0.5%
Other values (1248) 2776
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11382629
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:46.710885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088935048
Q10.016339869
median0.025898352
Q30.049426591
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033086722

Descriptive statistics

Standard deviation0.40822145
Coefficient of variation (CV)3.5863547
Kurtosis989.05906
Mean0.11382629
Median Absolute Deviation (MAD)0.012196886
Skewness24.87675
Sum337.83643
Variance0.16664476
MonotonicityNot monotonic
2023-03-06T18:25:46.862431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.02777777778 17
 
0.6%
0.0625 17
 
0.6%
0.02380952381 16
 
0.5%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.03571428571 13
 
0.4%
0.07692307692 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct213
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.888477
Minimum0
Maximum9014
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:47.024994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.86478
Coefficient of variation (CV)8.107685
Kurtosis596.20199
Mean34.888477
Median Absolute Deviation (MAD)1
Skewness21.975403
Sum103549
Variance80012.486
MonotonicityNot monotonic
2023-03-06T18:25:47.175635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (203) 705
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%
1594 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct1972
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235.78851
Minimum1
Maximum6009.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:47.328731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172
Q3281.375
95-th percentile598.345
Maximum6009.3333
Range6008.3333
Interquartile range (IQR)178.1375

Descriptive statistics

Standard deviation283.72375
Coefficient of variation (CV)1.2032976
Kurtosis103.07427
Mean235.78851
Median Absolute Deviation (MAD)82.625
Skewness7.7175389
Sum699820.29
Variance80499.168
MonotonicityNot monotonic
2023-03-06T18:25:47.480376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
60 8
 
0.3%
136 8
 
0.3%
130 7
 
0.2%
Other values (1962) 2881
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%
2082.225806 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct910
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.490391
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-03-06T18:25:47.641949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.035714
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.369048

Descriptive statistics

Standard deviation15.462077
Coefficient of variation (CV)0.88403267
Kurtosis29.303043
Mean17.490391
Median Absolute Deviation (MAD)6.6
Skewness3.4344414
Sum51911.482
Variance239.07584
MonotonicityNot monotonic
2023-03-06T18:25:47.798554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 43
 
1.4%
9 42
 
1.4%
16 41
 
1.4%
8 39
 
1.3%
17 37
 
1.2%
14 37
 
1.2%
7 36
 
1.2%
11 36
 
1.2%
5 34
 
1.1%
15 34
 
1.1%
Other values (900) 2589
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2023-03-06T18:25:42.061670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:24.206892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.745867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.283202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.952725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.504562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.218965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.926385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.544332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.227627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.873873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.422867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.190844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:24.352501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.882957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.416845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.083374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.648177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.356596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.043072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.690037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.358321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.998074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.552208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.324022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:24.484148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.998646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.543505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.203054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.774837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.494226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.160758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.833172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.492508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.125241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.695335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.461653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:24.616792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.129296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.674153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.324727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.910474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.627869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.310360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.982318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.638117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.260387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.841943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.582330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:24.738466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.243989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.797822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.436429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.032161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.751536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.458970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.116957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.764778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.388049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.966607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.718965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:24.874104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.375635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.962380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.620934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.171773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.959977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.643466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.266558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.914377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.530663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.114211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.872552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.011779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.506285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.111981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.769539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.316385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.101598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.791070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.420147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.058990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.668295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.254836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:42.992230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.127423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.623970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.257599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:29.908162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.448042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.230253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:34.916733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.548799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.188642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.791962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.376508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:43.124876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.253088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.763595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.407187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.028838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.687391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.396806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.042396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.692414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.335248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:39.922613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.517132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:43.258583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.381741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:26.910228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.552848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.154501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.839981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.531445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.164069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.833639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.479374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.058247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.660745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:43.396252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.496435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.027886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.672476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.263210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:31.960657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.661097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.290729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:36.960300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.604136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.176511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.792393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:43.536880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:25.622096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:27.154546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:28.807115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:30.385880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:32.090355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:33.795736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:35.417390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:37.094939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:38.742714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:40.302174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-06T18:25:41.929026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-06T18:25:47.934196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.0770.0010.026-0.0700.013-0.1310.019-0.002-0.064-0.123-0.016
gross_revenue-0.0771.000-0.4140.7720.9270.7460.245-0.2490.0910.3710.5750.106
recency_days0.001-0.4141.000-0.503-0.407-0.4360.0490.1090.018-0.118-0.0970.014
qtde_invoices0.0260.772-0.5031.0000.7180.6900.060-0.2580.0780.2940.101-0.182
qtde_items-0.0700.927-0.4070.7181.0000.7320.167-0.2280.0810.3440.7290.148
qtde_products0.0130.746-0.4360.6900.7321.000-0.377-0.1650.0350.2440.3850.515
avg_ticket-0.1310.2450.0490.0600.167-0.3771.000-0.1230.0920.1890.188-0.618
avg_recency_days0.019-0.2490.109-0.258-0.228-0.165-0.1231.000-0.881-0.398-0.0780.131
frequency-0.0020.0910.0180.0780.0810.0350.092-0.8811.0000.2350.028-0.122
qtde_returns-0.0640.371-0.1180.2940.3440.2440.189-0.3980.2351.0000.210-0.053
avg_basket_size-0.1230.575-0.0970.1010.7290.3850.188-0.0780.0280.2101.0000.404
avg_unique_basket_size-0.0160.1060.014-0.1820.1480.515-0.6180.131-0.122-0.0530.4041.000

Missing values

2023-03-06T18:25:43.730489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-06T18:25:43.983854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.2100372.000034.00001733.0000297.000018.152235.500017.000040.000050.97060.6176
1130473232.590056.00009.00001390.0000171.000018.904027.25000.028335.0000154.444411.6667
2125836705.38002.000015.00005028.0000232.000028.902523.18750.040350.0000335.20007.6000
313748948.250095.00005.0000439.000028.000033.866192.66670.01790.000087.80004.8000
415100876.0000333.00003.000080.00003.0000292.00008.60000.073222.000026.66670.3333
5152914623.300025.000014.00002102.0000102.000045.326523.20000.040129.0000150.14294.3571
6146885630.87007.000021.00003621.0000327.000017.219818.30000.0572399.0000172.42867.0476
7178095411.910016.000012.00002057.000061.000088.719835.70000.033541.0000171.41673.8333
81531160767.90000.000091.000038194.00002379.000025.54354.14440.2433474.0000419.71436.2308
9160982005.630087.00007.0000613.000067.000029.934847.66670.02440.000087.57144.8571
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
5626177271060.250015.00001.0000645.000066.000016.06446.00001.00006.0000645.000066.0000
563617232421.52002.00002.0000203.000036.000011.708912.00000.15380.0000101.500015.0000
563717468137.000010.00002.0000116.00005.000027.40004.00000.40000.000058.00002.5000
564813596697.04005.00002.0000406.0000166.00004.19907.00000.25000.0000203.000066.5000
5654148931237.85009.00002.0000799.000073.000016.95682.00000.66670.0000399.500036.0000
565812479473.200011.00001.0000382.000030.000015.77334.00001.000034.0000382.000030.0000
567914126706.13007.00003.0000508.000015.000047.07533.00000.750050.0000169.33334.6667
5685135211092.39001.00003.0000733.0000435.00002.51124.50000.30000.0000244.3333104.0000
569515060301.84008.00004.0000262.0000120.00002.51531.00002.00000.000065.500020.0000
571412558269.96007.00001.0000196.000011.000024.54186.00001.0000196.0000196.000011.0000